The DOI matches its Crossref record; 132 of the 132 references it cites were found in a registry or an index.
Visual Adaptation
Open accessAbstract
Sensory systems continuously mold themselves to the widely varying contexts in which they must operate. Studies of these adaptations have played a long and central role in vision science, partly because the specific adaptations remain a powerful tool for dissecting vision by exposing the mechanisms that are adapting. That is, “if it adapts, it's there.” Many insights about vision have come from this use of adaptation, as a method. A second important trend has been the realization that the processes of adaptation are themselves essential to how vision works and thus likely operate at all levels. That is, “if it's there, it adapts.” This observation has focused interest on the mechanisms of adaptation as the target rather than the probe. Together, these approaches have led to an emerging view of adaptation as a fundamental and ubiquitous coding strategy impacting all aspects of how we see.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Natural Image Statistics and Neural Representation
Simoncelli, E. P., & Olshausen, B. A. (2001) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.24.1.1193 · Cited by 2,460
Repetition and the brain: neural models of stimulus-specific effects
Grill-Spector, K., Henson, R., & Martin, A. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2005.11.006 · Cited by 2,256
Normalization as a canonical neural computation
Carandini, M., & Heeger, D. J. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3136 · Cited by 1,906
Error Correction, Sensory Prediction, and Adaptation in Motor Control
Shadmehr, R., Smith, M. A., & Krakauer, J. W. (2010) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-060909-153135 · Cited by 1,869
Synaptic depression and cortical gain control
Abbott, L. F., Varela, J. A., Sen, K., & Nelson, S. B. (1997) · Science · doi:10.1126/science.275.5297.221 · Cited by 1,690
Principles of sensorimotor learning
Wolpert, D. M., Diedrichsen, J., & Flanagan, J. R. (2011) · Nature Reviews Neuroscience · doi:10.1038/nrn3112 · Cited by 1,391
Adaptation-level theory : an experimental and systematic approach to behavior
Helson, H. (1964) · Harper & Row eBooks · Cited by 1,229
fMR-adaptation: a tool for studying the functional properties of human cortical neurons
Grill-Spector, K., & Malach, R. (2001) · Acta Psychologica · doi:10.1016/s0001-6918(01)00019-1 · Cited by 1,064
Predictive coding: a fresh view of inhibition in the retina
Srinivasan, M. V., Laughlin, S. B., & Dubs, A. (1982) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1982.0085 · Cited by 1,042 · cited 1×
“A final putative set of roles involves building predictions about the world (Srinivasan et al. 1982, Chopin & Mamassian 2012).”
BackgroundContinuous flash suppression reduces negative afterimages
Tsuchiya, N., & Koch, C. (2005) · Nature Neuroscience · doi:10.1038/nn1500 · Cited by 973
Cited by
Suboptimality in perceptual decision making
Rahnev, D., & Denison, R. N. (2018) · Behavioral and Brain Sciences · doi:10.1017/s0140525x18000936 · Cited by 342
Forms of prediction in the nervous system
Teufel, C., & Fletcher, P. C. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-020-0275-5 · Cited by 155
Virtual Reality Induced Symptoms and Effects: Concerns, Causes, Assessment & Mitigation
Conner, N. O., Freeman, H. R., Jones, J. A., Luczak, T., Carruth, D., Knight, A. C., & Chander, H. (2022) · Virtual Worlds · doi:10.3390/virtualworlds1020008 · Cited by 58
Metadata
- Authors
- Webster, M. A.
- Venue
- Annual Review of Vision Science
- Publisher
- Annual Reviews
- Published
- 2015-11-24
- Volume
- 1
- Issue
- 1
- Pages
- 547-567
- ISSN
- 2374-4642 (ISSN-L)
- 2374-4650
- OpenAlex
- W4237698667
- Cited by
- 334
- References
- 132
- FWCI
- 1.44
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Visual Adaptation. https://app.cochat.ai/papers/10.1146/annurev-vision-082114-035509
- The paper
- Webster, M. A. (2015). Visual Adaptation. Annual Review of Vision Science. https://doi.org/10.1146/annurev-vision-082114-035509
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
